Overview

Dataset statistics

Number of variables14
Number of observations604
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.7 KiB
Average record size in memory223.2 B

Variable types

NUM13
DATE1

Warnings

me_avg_temp is highly correlated with vt_avg_temp and 2 other fieldsHigh correlation
vt_avg_temp is highly correlated with me_avg_temp and 2 other fieldsHigh correlation
ct_avg_temp is highly correlated with vt_avg_temp and 2 other fieldsHigh correlation
ma_avg_temp is highly correlated with vt_avg_temp and 2 other fieldsHigh correlation
date is uniformly distributed Uniform
df_index has unique values Unique
vt_avg_temp has 7 (1.2%) zeros Zeros
vt_prcp has 366 (60.6%) zeros Zeros
me_avg_temp has 8 (1.3%) zeros Zeros
me_prcp has 371 (61.4%) zeros Zeros
ct_prcp has 404 (66.9%) zeros Zeros
ma_prcp has 381 (63.1%) zeros Zeros
CT_conf_cases has 48 (7.9%) zeros Zeros
ME_conf_cases has 50 (8.3%) zeros Zeros
MA_conf_cases has 7 (1.2%) zeros Zeros
VT_conf_cases has 46 (7.6%) zeros Zeros

Reproduction

Analysis started2021-11-06 15:27:06.007968
Analysis finished2021-11-06 15:27:22.686042
Duration16.68 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct604
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.5
Minimum21
Maximum624
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-06T11:27:22.772481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile51.15
Q1171.75
median322.5
Q3473.25
95-th percentile593.85
Maximum624
Range603
Interquartile range (IQR)301.5

Descriptive statistics

Standard deviation174.5040592
Coefficient of variation (CV)0.5410978579
Kurtosis-1.2
Mean322.5
Median Absolute Deviation (MAD)151
Skewness0
Sum194790
Variance30451.66667
MonotocityStrictly increasing
2021-11-06T11:27:22.886649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2110.2%
 
41810.2%
 
42010.2%
 
42110.2%
 
42210.2%
 
42310.2%
 
42410.2%
 
42510.2%
 
42610.2%
 
42710.2%
 
42810.2%
 
42910.2%
 
43010.2%
 
43110.2%
 
43210.2%
 
43310.2%
 
43410.2%
 
41910.2%
 
41710.2%
 
43610.2%
 
41610.2%
 
40110.2%
 
40210.2%
 
40310.2%
 
40410.2%
 
Other values (579)57995.9%
 
ValueCountFrequency (%) 
2110.2%
 
2210.2%
 
2310.2%
 
2410.2%
 
2510.2%
 
2610.2%
 
2710.2%
 
2810.2%
 
2910.2%
 
3010.2%
 
ValueCountFrequency (%) 
62410.2%
 
62310.2%
 
62210.2%
 
62110.2%
 
62010.2%
 
61910.2%
 
61810.2%
 
61710.2%
 
61610.2%
 
61510.2%
 

date
Date

UNIFORM

Distinct600
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum2020-01-22 00:00:00
Maximum2021-09-12 00:00:00
2021-11-06T11:27:22.990738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:23.102383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vt_avg_temp
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct199
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.8129139
Minimum-166
Maximum294
Zeros7
Zeros (%)1.2%
Memory size4.8 KiB
2021-11-06T11:27:23.210718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-166
5-th percentile-82.55
Q117
median108
Q3200
95-th percentile264
Maximum294
Range460
Interquartile range (IQR)183

Descriptive statistics

Standard deviation110.4899225
Coefficient of variation (CV)1.044200736
Kurtosis-0.9476922395
Mean105.8129139
Median Absolute Deviation (MAD)92
Skewness-0.2632434609
Sum63911
Variance12208.02298
MonotocityNot monotonic
2021-11-06T11:27:23.309539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
233111.8%
 
200101.7%
 
4481.3%
 
22281.3%
 
8381.3%
 
6181.3%
 
19481.3%
 
1771.2%
 
10871.2%
 
26471.2%
 
7571.2%
 
071.2%
 
17571.2%
 
19761.0%
 
27261.0%
 
22761.0%
 
1461.0%
 
2861.0%
 
22861.0%
 
24161.0%
 
10361.0%
 
18061.0%
 
23061.0%
 
18361.0%
 
19161.0%
 
Other values (174)42971.0%
 
ValueCountFrequency (%) 
-16610.2%
 
-15510.2%
 
-15210.2%
 
-14910.2%
 
-14710.2%
 
-14410.2%
 
-14120.3%
 
-13810.2%
 
-12410.2%
 
-12110.2%
 
ValueCountFrequency (%) 
29410.2%
 
29120.3%
 
28910.2%
 
28630.5%
 
28330.5%
 
28020.3%
 
27810.2%
 
27530.5%
 
27261.0%
 
26930.5%
 

vt_prcp
Real number (ℝ≥0)

ZEROS

Distinct72
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53145695
Minimum0
Maximum635
Zeros366
Zeros (%)60.6%
Memory size4.8 KiB
2021-11-06T11:27:23.408199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile136.7
Maximum635
Range635
Interquartile range (IQR)13

Descriptive statistics

Standard deviation58.18653031
Coefficient of variation (CV)2.582457514
Kurtosis29.31053223
Mean22.53145695
Median Absolute Deviation (MAD)0
Skewness4.545332503
Sum13609
Variance3385.672309
MonotocityNot monotonic
2021-11-06T11:27:23.503855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
036660.6%
 
3366.0%
 
5254.1%
 
10162.6%
 
13101.7%
 
891.5%
 
1581.3%
 
1871.2%
 
3061.0%
 
2850.8%
 
3650.8%
 
3850.8%
 
2050.8%
 
2540.7%
 
6140.7%
 
5340.7%
 
2340.7%
 
8640.7%
 
17030.5%
 
5130.5%
 
5630.5%
 
8430.5%
 
10230.5%
 
14220.3%
 
14020.3%
 
Other values (47)6210.3%
 
ValueCountFrequency (%) 
036660.6%
 
3366.0%
 
5254.1%
 
891.5%
 
10162.6%
 
13101.7%
 
1581.3%
 
1871.2%
 
2050.8%
 
2340.7%
 
ValueCountFrequency (%) 
63510.2%
 
38410.2%
 
35610.2%
 
33310.2%
 
32010.2%
 
31510.2%
 
29210.2%
 
24910.2%
 
24110.2%
 
22910.2%
 

me_avg_temp
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct185
Distinct (%)30.7%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean73.04477612
Minimum-188
Maximum267
Zeros8
Zeros (%)1.3%
Memory size4.8 KiB
2021-11-06T11:27:23.603035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-188
5-th percentile-105
Q1-11
median80
Q3167
95-th percentile229.8
Maximum267
Range455
Interquartile range (IQR)178

Descriptive statistics

Standard deviation108.3618568
Coefficient of variation (CV)1.483499061
Kurtosis-1.044198411
Mean73.04477612
Median Absolute Deviation (MAD)91
Skewness-0.1940319841
Sum44046
Variance11742.29201
MonotocityNot monotonic
2021-11-06T11:27:23.701411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18691.5%
 
20091.5%
 
081.3%
 
10081.3%
 
13381.3%
 
-1181.3%
 
381.3%
 
8671.2%
 
9471.2%
 
17571.2%
 
-7471.2%
 
-5871.2%
 
18371.2%
 
1171.2%
 
12271.2%
 
-3671.2%
 
14761.0%
 
-3061.0%
 
18061.0%
 
7561.0%
 
15261.0%
 
15361.0%
 
21161.0%
 
18961.0%
 
6461.0%
 
Other values (160)42870.9%
 
ValueCountFrequency (%) 
-18810.2%
 
-16910.2%
 
-16320.3%
 
-16010.2%
 
-15810.2%
 
-15510.2%
 
-14910.2%
 
-14430.5%
 
-13810.2%
 
-13320.3%
 
ValueCountFrequency (%) 
26720.3%
 
26610.2%
 
26110.2%
 
25820.3%
 
25510.2%
 
25030.5%
 
24710.2%
 
24430.5%
 
24210.2%
 
24140.7%
 

me_prcp
Real number (ℝ≥0)

ZEROS

Distinct78
Distinct (%)12.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.2106136
Minimum0
Maximum1311
Zeros371
Zeros (%)61.4%
Memory size4.8 KiB
2021-11-06T11:27:23.799473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile151.5
Maximum1311
Range1311
Interquartile range (IQR)13

Descriptive statistics

Standard deviation91.36877426
Coefficient of variation (CV)3.127930673
Kurtosis75.94114096
Mean29.2106136
Median Absolute Deviation (MAD)0
Skewness7.185036492
Sum17614
Variance8348.25291
MonotocityNot monotonic
2021-11-06T11:27:23.903190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
037161.4%
 
3315.1%
 
5193.1%
 
8172.8%
 
10111.8%
 
18101.7%
 
30101.7%
 
1571.2%
 
6661.0%
 
1361.0%
 
2061.0%
 
2350.8%
 
4640.7%
 
2840.7%
 
5640.7%
 
11430.5%
 
8930.5%
 
3330.5%
 
4830.5%
 
2530.5%
 
10430.5%
 
5330.5%
 
5820.3%
 
50320.3%
 
6920.3%
 
Other values (53)6510.8%
 
ValueCountFrequency (%) 
037161.4%
 
3315.1%
 
5193.1%
 
8172.8%
 
10111.8%
 
1361.0%
 
1571.2%
 
18101.7%
 
2061.0%
 
2350.8%
 
ValueCountFrequency (%) 
131110.2%
 
68810.2%
 
60210.2%
 
58210.2%
 
50320.3%
 
39110.2%
 
37310.2%
 
36110.2%
 
35310.2%
 
31810.2%
 

ct_avg_temp
Real number (ℝ)

HIGH CORRELATION

Distinct269
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.1887417
Minimum-113
Maximum298
Zeros1
Zeros (%)0.2%
Memory size4.8 KiB
2021-11-06T11:27:24.007540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-113
5-th percentile-32
Q140
median130.5
Q3212
95-th percentile260.7
Maximum298
Range411
Interquartile range (IQR)172

Descriptive statistics

Standard deviation97.18077804
Coefficient of variation (CV)0.7888771058
Kurtosis-1.099907942
Mean123.1887417
Median Absolute Deviation (MAD)84.5
Skewness-0.1745432988
Sum74406
Variance9444.103621
MonotocityNot monotonic
2021-11-06T11:27:24.104621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21991.5%
 
4671.2%
 
3871.2%
 
361.0%
 
24861.0%
 
23161.0%
 
2650.8%
 
3150.8%
 
19550.8%
 
18750.8%
 
20850.8%
 
23850.8%
 
11450.8%
 
25450.8%
 
22250.8%
 
22150.8%
 
13250.8%
 
440.7%
 
5740.7%
 
3340.7%
 
22740.7%
 
8340.7%
 
26140.7%
 
24140.7%
 
21240.7%
 
Other values (244)47678.8%
 
ValueCountFrequency (%) 
-11310.2%
 
-10510.2%
 
-10310.2%
 
-9110.2%
 
-8810.2%
 
-7610.2%
 
-7010.2%
 
-6710.2%
 
-5910.2%
 
-5820.3%
 
ValueCountFrequency (%) 
29820.3%
 
29110.2%
 
28710.2%
 
28620.3%
 
28110.2%
 
28020.3%
 
27720.3%
 
27610.2%
 
27510.2%
 
27410.2%
 

ct_prcp
Real number (ℝ≥0)

ZEROS

Distinct88
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.58278146
Minimum0
Maximum1034
Zeros404
Zeros (%)66.9%
Memory size4.8 KiB
2021-11-06T11:27:24.207108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile227.8
Maximum1034
Range1034
Interquartile range (IQR)13

Descriptive statistics

Standard deviation101.9906091
Coefficient of variation (CV)2.86629108
Kurtosis32.59421324
Mean35.58278146
Median Absolute Deviation (MAD)0
Skewness4.96775597
Sum21492
Variance10402.08435
MonotocityNot monotonic
2021-11-06T11:27:24.514395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
040466.9%
 
3213.5%
 
5122.0%
 
881.3%
 
1871.2%
 
5171.2%
 
2871.2%
 
4150.8%
 
2050.8%
 
1550.8%
 
1050.8%
 
3340.7%
 
4340.7%
 
1340.7%
 
3830.5%
 
3630.5%
 
4630.5%
 
12430.5%
 
6430.5%
 
2520.3%
 
9120.3%
 
6620.3%
 
18520.3%
 
8620.3%
 
2320.3%
 
Other values (63)7913.1%
 
ValueCountFrequency (%) 
040466.9%
 
3213.5%
 
5122.0%
 
881.3%
 
1050.8%
 
1340.7%
 
1550.8%
 
1871.2%
 
2050.8%
 
2320.3%
 
ValueCountFrequency (%) 
103410.2%
 
87910.2%
 
81310.2%
 
60510.2%
 
53810.2%
 
47820.3%
 
43410.2%
 
42410.2%
 
41110.2%
 
40410.2%
 

ma_avg_temp
Real number (ℝ)

HIGH CORRELATION

Distinct163
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.9387417
Minimum-100
Maximum311
Zeros6
Zeros (%)1.0%
Memory size4.8 KiB
2021-11-06T11:27:24.615639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-25
Q146.25
median125
Q3203
95-th percentile267
Maximum311
Range411
Interquartile range (IQR)156.75

Descriptive statistics

Standard deviation94.03503752
Coefficient of variation (CV)0.7587218993
Kurtosis-1.090883354
Mean123.9387417
Median Absolute Deviation (MAD)78
Skewness-0.06998309705
Sum74859
Variance8842.588281
MonotocityNot monotonic
2021-11-06T11:27:24.710505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50152.5%
 
200122.0%
 
58101.7%
 
186101.7%
 
16991.5%
 
18391.5%
 
3091.5%
 
25091.5%
 
1981.3%
 
8081.3%
 
23381.3%
 
23681.3%
 
13981.3%
 
20881.3%
 
4481.3%
 
6471.2%
 
24471.2%
 
13671.2%
 
9771.2%
 
3671.2%
 
19471.2%
 
8371.2%
 
10271.2%
 
23061.0%
 
18961.0%
 
Other values (138)39765.7%
 
ValueCountFrequency (%) 
-10010.2%
 
-9810.2%
 
-8110.2%
 
-7010.2%
 
-5910.2%
 
-5620.3%
 
-5320.3%
 
-4810.2%
 
-4710.2%
 
-4510.2%
 
ValueCountFrequency (%) 
31110.2%
 
30310.2%
 
30210.2%
 
29430.5%
 
28910.2%
 
28810.2%
 
28330.5%
 
28110.2%
 
28040.7%
 
27810.2%
 

ma_prcp
Real number (ℝ≥0)

ZEROS

Distinct92
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.82450331
Minimum0
Maximum950
Zeros381
Zeros (%)63.1%
Memory size4.8 KiB
2021-11-06T11:27:24.809811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile191
Maximum950
Range950
Interquartile range (IQR)20

Descriptive statistics

Standard deviation102.7435375
Coefficient of variation (CV)2.716322184
Kurtosis26.06332338
Mean37.82450331
Median Absolute Deviation (MAD)0
Skewness4.583932541
Sum22846
Variance10556.23449
MonotocityNot monotonic
2021-11-06T11:27:24.906292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
038163.1%
 
3172.8%
 
5142.3%
 
8111.8%
 
1881.3%
 
2571.2%
 
2071.2%
 
5171.2%
 
1361.0%
 
1561.0%
 
11461.0%
 
2861.0%
 
12261.0%
 
1050.8%
 
8950.8%
 
3850.8%
 
6940.7%
 
7640.7%
 
4340.7%
 
5630.5%
 
4830.5%
 
5830.5%
 
8130.5%
 
3630.5%
 
10220.3%
 
Other values (67)7812.9%
 
ValueCountFrequency (%) 
038163.1%
 
3172.8%
 
5142.3%
 
8111.8%
 
1050.8%
 
1361.0%
 
1561.0%
 
1881.3%
 
2071.2%
 
2320.3%
 
ValueCountFrequency (%) 
95010.2%
 
85910.2%
 
64810.2%
 
63810.2%
 
61710.2%
 
54410.2%
 
52610.2%
 
49810.2%
 
48810.2%
 
45710.2%
 

CT_conf_cases
Real number (ℝ≥0)

ZEROS

Distinct418
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163638.8311
Minimum0
Maximum378933
Zeros48
Zeros (%)7.9%
Memory size4.8 KiB
2021-11-06T11:27:25.002523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145745
median98204.5
Q3327473.5
95-th percentile361754.7
Maximum378933
Range378933
Interquartile range (IQR)281728.5

Descriptive statistics

Standard deviation139829.8753
Coefficient of variation (CV)0.8545030192
Kurtosis-1.640406777
Mean163638.8311
Median Absolute Deviation (MAD)98196.5
Skewness0.2992419531
Sum98837854
Variance1.955239401e+10
MonotocityNot monotonic
2021-11-06T11:27:25.105115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0487.9%
 
11258161.0%
 
34734140.7%
 
5336540.7%
 
17274340.7%
 
34947640.7%
 
37513540.7%
 
22342230.5%
 
4728730.5%
 
5032030.5%
 
27994630.5%
 
25002330.5%
 
5658730.5%
 
6283030.5%
 
5552730.5%
 
35067530.5%
 
14676130.5%
 
5151930.5%
 
7812530.5%
 
20599430.5%
 
25937230.5%
 
34835030.5%
 
5829730.5%
 
5249530.5%
 
6003830.5%
 
Other values (393)47678.8%
 
ValueCountFrequency (%) 
0487.9%
 
110.2%
 
310.2%
 
510.2%
 
1110.2%
 
2310.2%
 
2410.2%
 
4110.2%
 
6810.2%
 
9610.2%
 
ValueCountFrequency (%) 
37893330.5%
 
37830810.2%
 
37768210.2%
 
37674710.2%
 
37513540.7%
 
37447010.2%
 
37378410.2%
 
37307210.2%
 
37206910.2%
 
37070830.5%
 

ME_conf_cases
Real number (ℝ≥0)

ZEROS

Distinct524
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27132.15728
Minimum0
Maximum80513
Zeros50
Zeros (%)8.3%
Memory size4.8 KiB
2021-11-06T11:27:25.216044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12952.25
median9626.5
Q355519
95-th percentile72300.9
Maximum80513
Range80513
Interquartile range (IQR)52566.75

Descriptive statistics

Standard deviation28277.32178
Coefficient of variation (CV)1.042206909
Kurtosis-1.396779325
Mean27132.15728
Median Absolute Deviation (MAD)9626.5
Skewness0.5491075417
Sum16387823
Variance799606927.1
MonotocityNot monotonic
2021-11-06T11:27:25.320927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0508.3%
 
1150840.7%
 
7538130.5%
 
7366030.5%
 
6922030.5%
 
7807130.5%
 
7046330.5%
 
6912130.5%
 
7252230.5%
 
6990530.5%
 
6947630.5%
 
2154720.3%
 
3659820.3%
 
30320.3%
 
320.3%
 
96520.3%
 
1175720.3%
 
463320.3%
 
7130620.3%
 
1126520.3%
 
8051320.3%
 
4163610.2%
 
2524510.2%
 
272110.2%
 
6826210.2%
 
Other values (499)49982.6%
 
ValueCountFrequency (%) 
0508.3%
 
110.2%
 
210.2%
 
320.3%
 
1710.2%
 
2610.2%
 
3510.2%
 
5210.2%
 
5610.2%
 
7010.2%
 
ValueCountFrequency (%) 
8051320.3%
 
7992910.2%
 
7942310.2%
 
7880310.2%
 
7807130.5%
 
7806910.2%
 
7757810.2%
 
7691310.2%
 
7629010.2%
 
7585610.2%
 

MA_conf_cases
Real number (ℝ≥0)

ZEROS

Distinct534
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337565.553
Minimum0
Maximum777022
Zeros7
Zeros (%)1.2%
Memory size4.8 KiB
2021-11-06T11:27:25.432167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1102706.25
median197209
Q3665473.75
95-th percentile734291.3
Maximum777022
Range777022
Interquartile range (IQR)562767.5

Descriptive statistics

Standard deviation281949.0725
Coefficient of variation (CV)0.8352424292
Kurtosis-1.637740772
Mean337565.553
Median Absolute Deviation (MAD)197017.5
Skewness0.295047129
Sum203889594
Variance7.949527947e+10
MonotocityNot monotonic
2021-11-06T11:27:25.537985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2244.0%
 
181.3%
 
071.2%
 
76558440.7%
 
22496440.7%
 
71013840.7%
 
71227230.5%
 
73448630.5%
 
71518030.5%
 
77702230.5%
 
71978030.5%
 
74373530.5%
 
72639530.5%
 
71083030.5%
 
75379530.5%
 
37517820.3%
 
34192520.3%
 
64323220.3%
 
21459420.3%
 
22613220.3%
 
70694520.3%
 
320.3%
 
15970710.2%
 
2012710.2%
 
63558010.2%
 
Other values (509)50984.3%
 
ValueCountFrequency (%) 
071.2%
 
181.3%
 
2244.0%
 
320.3%
 
410.2%
 
610.2%
 
1410.2%
 
2810.2%
 
7210.2%
 
9110.2%
 
ValueCountFrequency (%) 
77702230.5%
 
77514910.2%
 
77274210.2%
 
77126710.2%
 
76558440.7%
 
76367310.2%
 
76190610.2%
 
75986910.2%
 
75809410.2%
 
75379530.5%
 

VT_conf_cases
Real number (ℝ≥0)

ZEROS

Distinct513
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9668.084437
Minimum0
Maximum30114
Zeros46
Zeros (%)7.6%
Memory size4.8 KiB
2021-11-06T11:27:25.647711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11156
median3235.5
Q321532
95-th percentile26016.45
Maximum30114
Range30114
Interquartile range (IQR)20376

Descriptive statistics

Standard deviation10197.60778
Coefficient of variation (CV)1.054770244
Kurtosis-1.333775532
Mean9668.084437
Median Absolute Deviation (MAD)3235.5
Skewness0.6089203436
Sum5839523
Variance103991204.4
MonotocityNot monotonic
2021-11-06T11:27:25.743789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0467.6%
 
410040.7%
 
2441940.7%
 
140.7%
 
2604040.7%
 
2436030.5%
 
2445730.5%
 
2425230.5%
 
2467630.5%
 
2432030.5%
 
2488930.5%
 
2439230.5%
 
2455030.5%
 
2532030.5%
 
92720.3%
 
94020.3%
 
417220.3%
 
94420.3%
 
86220.3%
 
170220.3%
 
163720.3%
 
136620.3%
 
741220.3%
 
382720.3%
 
678120.3%
 
Other values (488)49381.6%
 
ValueCountFrequency (%) 
0467.6%
 
140.7%
 
220.3%
 
520.3%
 
1220.3%
 
1310.2%
 
2210.2%
 
2910.2%
 
4910.2%
 
5210.2%
 
ValueCountFrequency (%) 
3011410.2%
 
2993410.2%
 
2973510.2%
 
2958810.2%
 
2943610.2%
 
2932510.2%
 
2912510.2%
 
2899410.2%
 
2888410.2%
 
2870310.2%
 

Interactions

2021-11-06T11:27:07.507152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:07.690875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:07.776218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:07.863080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:07.943916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.027667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.107230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.187750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.269950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.350598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.440242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.535641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.626409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.710842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.793506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.870750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:08.948433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.025146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.103153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.175765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.250626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.326580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.399276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.478521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.564794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.647620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:09.730833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:10.146725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.232023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.311297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.393499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.472685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.554604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.640791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.732533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.820999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:10.903636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:11.281364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.358125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:11.511688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.590011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.674973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.758891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.843274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:11.920525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.000748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.078659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:12.322319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.398666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.479574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.561824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.737713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.824018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:12.911710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.002039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.085836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.162788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.236593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:13.462224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.534713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.610311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.683932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:13.841842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:13.929498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:14.015093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:14.178147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:14.881569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:14.965388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:15.051923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:15.131549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:15.208590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:15.281313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:15.582950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:15.657628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:15.888101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:16.094629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:16.487360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:16.639086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:17.362603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:17.448284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:17.539065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:18.727043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:18.814620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:18.903884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:18.989507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.076315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-06T11:27:19.546890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.636949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.726605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.811848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.900539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:19.986105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.075234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.161146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.411839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.497082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.587312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.680256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.778751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.901226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:20.999151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.081563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.161993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.245241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.325159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.406996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.485368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.565031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.647420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.725535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.813380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.904708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:21.995175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-11-06T11:27:25.835709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-06T11:27:25.965453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-06T11:27:26.088275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-06T11:27:26.215136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-06T11:27:22.184561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:22.393198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:22.521302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-06T11:27:22.587348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexdatevt_avg_tempvt_prcpme_avg_tempme_prcpct_avg_tempct_prcpma_avg_tempma_prcpCT_conf_casesME_conf_casesMA_conf_casesVT_conf_cases
0212020-01-22-280-72.00.0-76.00.0-53.00.00000
1222020-01-23-270-30.03.0-46.00.00.00.00000
2232020-01-24-80-14.00.06.00.019.00.00000
3242020-01-253119-30.03.023.0218.033.025.00000
4252020-01-26222311.0132.049.00.064.0193.00000
5262020-01-271413-3.010.038.00.039.00.00000
6272020-01-28-113-58.00.026.00.030.00.00000
7282020-01-29-690-105.00.013.00.014.00.00010
8292020-01-30-850-96.00.0-32.00.0-34.00.00010
9302020-01-31-530-55.00.0-17.03.02.00.00010

Last rows

df_indexdatevt_avg_tempvt_prcpme_avg_tempme_prcpct_avg_tempct_prcpma_avg_tempma_prcpCT_conf_casesME_conf_casesMA_conf_casesVT_conf_cases
5946152021-09-031830150.020.0171.00.0169.00.03751357757876558428703
5956162021-09-041940141.00.0181.00.0189.00.03751357806976558428884
5966172021-09-051860122.025.0174.013.0186.03.03751357807176558428994
5976182021-09-06178188175.097.0219.00.0233.00.03751357807176558429125
5986192021-09-071890152.00.0192.00.0200.00.03767477807177126729325
5996202021-09-08227241175.00.0212.00.0222.00.03776827880377274229436
6006212021-09-091970169.0353.0219.038.0216.081.03783087942377514929588
6016222021-09-101555147.0130.0194.00.0203.076.03789337992977702229735
6026232021-09-111770152.00.0173.00.0178.00.03789338051377702229934
6036242021-09-122083175.00.0208.00.0219.00.03789338051377702230114